Lyme rashes disease classification using deep feature fusion technique

Abstract Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists’ probe and investigate Lyme skin rashes effectively. This paper proposes a new in‐depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state‐of‐the‐art models.

rates are in Central and Western Europe features from data.Over the past decades, the accuracy (34.5%) and Asia (19.0%), and the most vulnerable are men aged 50 and up living in rural areas. 13The CDC estimated that approximately 300 000 people get Lyme disease yearly in Columbia. 14Lyme disease incidence in the United Kingdom is expected to double between 2020 and 2031, compared to 2011 to 2020. 9rious methods have been developed to diagnose Lyme disease. 15metimes symptoms overlap with those of other diseases, making diagnosis even for a seasoned rheumatologist difficult.Laboratory tests, such as the Western blot test, are used to find antibodies for a conclusive diagnosis 16 after detecting potentially infected ticks.Medicine specialists recommended inappropriate antibiotics or unusual therapy for Lyme disease that caused cholecystitis, catheterassociated bloodstream infection, venous catheter clogs, and fatality. 17en in a developed country like the USA, less than 300 rheumatologists make them a very scarce specialty. 18The manual identification of Lyme disease is a callous and tiring process for patients. 19In addition, the right diagnosis is rare and heavily dependent on the physician's skills, 20 and this treatment requires a diagnosis to lessen mortality loss. 18The inability of medical personnel to analyze Lyme disease processes is due to a lack of diagnostic expertise and the absence of a professional, computer-aided diagnosis. 21Therefore, we must concentrate on developing more advanced medical technologies and an autonomous system that can identify Lyme disease quickly, accurately, and with minimal possibility of error.
Deep learning is a machine learning type that involves using artificial neural networks with multiple processing layers, 22 which can automatically learn and extract features from data.Over the past 2 decades, the accuracy of disease predictions has grown by 15% to 20% because of machine learning (ML) algorithms. 23In the context of erythema migrans rash classification, deep learning methods could be used to extract features from images of rashes and use those features to classify the rashes as either indicative of Lyme disease or not. 24ep feature fusion 25 is a method proposed for this purpose, which merges the features extracted by different deep learning models to boost classification accuracy. 26Theoretically, this method could produce a more accurate representation of rash photos for classification purposes.Selecting and merging features is what feature fusion is all about; it is about how to eliminate extraneous or unimportant features. 27If the distributions of two traits are the same or nearly identical, they are redundant.A low correlation between a feature and the classes makes it irrelevant.Fusion of the remaining features yields an improved feature set, which is then fed into a classifier to produce the final result.As a result, to improve our classifications' precision, we focus on fusing decisions and features.However, the results of this study show that deep feature fusion is useful for rash classification in cases of erythema migrans.The best feature fusion techniques and deep learning models for erythema rash categorization are identified.
The primary results of this research are as follows: 1. To develop a new feature fusion method based on deep neural networks that can differentiate between patients with Lyme positive (LP) infection and those with other disorders Lyme negative (LN).
2. To enhance the Lyme disease rashes, add images of patients collected from the different hospitals in Lahore, Pakistan.
3. To develop and boost the proposed method's performance in terms of classification accuracy.

The organization of this paper is as follows:
The following section briefly introduces the related works.Section 3 explains the materials and methods, and the architecture of the proposed approach.
Section 4 provides experimental results and discussion where the effectiveness of the proposed scheme is compared to the existing schemes.

RELATED WORK
A few studies have explored using deep learning to classify erythema migrans rashes associated with Lyme disease.In prior research, we found that using deep feature fusion improved the performance of the classification task compared to using a single CNN model.Overall, these studies suggest that deep learning methods, including the use of deep feature fusion, have the potential to be effective for erythema migrans rash classification in the context of Lyme disease diagnosis.Also, the evidence suggests that prior datasets were not legit and authenticated by any skin doctor.There is a need to increase classification accuracy.However, more research is needed to fully understand the optimal approaches for using deep TA B L E 2 Lyme disease rashes dataset summary.

Class labels Samples
Lyme positive 392

Lyme negative 318
Total 700 learning in this context also, as shown related work summary in Table 1.

MATERIALS AND METHODS
Having a relevant and accurate dataset for the task at hand is crucial to the success of deep learning methodologies.The subsequent dataset is used in this study.

Dataset
Regarding the proposed procedure, experiments were carried out using the dataset obtained from Kaggle, which can be accessed online, named Lyme disease rashes. 51This dataset contains images of the infected patient's skin worms.Initially, 400 photos displaying common Lyme disease symptoms were found in the Lyme rashes disease dataset.
Of these, 200 showed Lyme-positive symptoms, whereas 200 showed Lyme-negative signs.Because this dataset has several issues (Image labeling, Enhancement, etc.), which we identified after previewing it, we added an extra 192 Lyme-positive and 118 Lyme-negative photos collected from skin doctors from different hospitals in Lahore, Pakistan, and both of them verified all of the images in the dataset as depicted in Table 2 and the Figure 1.

Image preprocessing
Preprocessing is used on all Lyme disease input photos to achieve more uniformity in classification results and better features.The model's performance will drastically decrease, but the processing time will be cut in half.

Data augmentation
Various data augmentation strategies were applied to prevent over- Because of this, we used a channel shift range of 0.05 and settled on fill mode to get the closest possible reproduction of the source material, as shown in Table 3.

Dataset splitting
Lyme disease rash data was partitioned into a training, validation, and test set.Although the model was developed with the help of the training dataset, it was checked for accuracy using the validation set.
Once the model was trained, it was tested on unseen photos (test set).

Methods used Setting
Scaling 0 to 1 range Rotation 25

The proposed methodology
The development of deep learning technologies has brought about a revolution in machine learning and digital image processing.The ability of images to develop consistently, discriminate, and functional semantic properties give deep learning methods their potency.When we talk about making a network "deep," we mean it has several layers.
Not only is deep learning excellent at recognizing facial expressions, 52 but it has also shown promise in other areas, such as smart city planning, 53 skin cancer diagnosis, 54  At the same time, the base model will remain frozen.The obtained in-depth features concatenate them.This approach is called feature fusion.

3.6
The architecture of the proposed deep feature fusion model Any pattern recognition system aims to develop a highly accurate classification model for a given problem, such as diagnosing Lyme disease based on a patient's rash.Three initial models will be trained using Lyme disease rash symptoms data.The feature is extracted from a fully connected (Dense) layer, the output of which is a 2048-dimensional vector in InceptionV3.A vector of 1000 dimensions represents each image's projected class scores.Features will be retrieved from the final pooling layer in the Xception model.It includes 2048 unique 8 × 8 map designs.
DenseNet201, the global average pooling GAP, uses this layer as its penultimate pooling layer before the final fully connected layer, eliminating all remaining channels in the feature maps to produce an output of 1 × 1xC.It was repeated thrice with different parameters based on the results from the other locations and periods.An additional stage of meta-classifier fusion is implemented.In the next phase, a novel feature fusion model will combine the extracted feature with others.We found that our model outperformed the state-of-the-art methods.When we start working on feature fusion, all our pre-trained models will stop receiving new data.The network's results are highly competitive with those from more traditional approaches.It is common practice to apply multiple classification models for a given pattern recognition job, each based on a different theory or set of methods, and then choose the one with the best overall performance.However, it was demonstrated that even if one model has the highest accuracy, the collections of patterns mistakenly classified by the multiple classifiers would only sometimes overlap.For this reason, adopting an ensemble approach with many classifiers can improve performance. 55This is because each classifier brings its distinct understanding of the classified patterns.
Deep feature fusion-based learning uses several agreeing models, as depicted in Figure 2, rather than relying on a single model to make a call.
After applying a cutoff to the classifier output probabilities (estimates of the posterior probability of the class), the hard-level combination maps these probabilities into class labels. 56The majority voting technique considers the input from all classifiers that comprise a given hard-level combination.The majority vote determines which category will be chosen.Deep feature level fusion has great potential to improve classification performance 57,58 since it combines many feature sets produced from different feature extractors.When many perspectives (in-depth features obtained from multiple CNNs) must be represented, feature-level fusion often involves concatenating numerous normalized feature subsets into a single feature vector.
Concerning the CNN-based feature-level fusion studies, the finetuning of these CNN models using the same target dataset (the Lyme rashes disease dataset in our study) consisting of concatenated feature vectors can provide supplementary information. 59It is true even if the various CNN models are based on different configurations (or architectures).Feature fusion's numerical expression shifts based on the technique employed.Concatenation is a common technique for feature fusion, in which features from several sources are joined to create a new feature vector.This procedure can be expressed mathematically as: F f used is the fused feature vector, and F 1 , F 2 , . .., and F n are the individual feature vectors from the different sources.The semi-colon (;) represents concatenation.
Another standard method for feature fusion is element-wise addition or averaging.The mathematical expression for this method would be: F f used is the fused feature vector, and F 1 , F 2 , . .., and F n are the individual feature vectors from the different sources.The plus (+) sign represents element-wise addition, and the division by n represents averaging.A more complex method is feature fusion through a Neural Network, where the feature vectors are passed through a neural network that learns to combine them optimally.In this case, the mathematical expression would depend on the neural network's architecture but involve matrix multiplication, activation functions, and biases.It's important to note that the feature fusion process can also be performed using a combination of different methods, and the mathematical expression will depend on the specific implementation.

3.6.1
The first base model: InceptionV3 Szegedy et al. 60 proposed an InceptionV3 model that employs a threeinception-block architecture, all of which feature parallel convolutions.
Modules like this help relieve the over-fitting issue and improve the computational efficiency of deep architecture.Annually, researchers in the Lyme disease image recognition and classification compete using 1.4 million photos from 1000 object classes for the ImageNet Large Scale Visual Identification Challenge (ILSVRC). 61

3.6.2
The second base model: DenseNet201 It is a technique known as feed-forward; all the layers that make up a Dense Convolutional Network (DenseNet) are connected.The number of feature maps in DenseNet is relatively low (12 filters per layer), and the layer itself is relatively thin (12 filters per layer). 64e capabilities of DenseNet reduce the total number of parameters, and it has a minimal effect on the gradient problem, feature deployment, and feature reuse. 65  to change it; however, if the value is higher, it will not be changed and will continue to be used as is.At this stage, a ReLu-activated matrix image is multiplied by a convolution matrix with a 3 × 3 filter.Another processed matrix will be the resultant value.

The third base model: Xception
This research uses only depth-separable convolution layers to create a neural network architecture for convolutional learning.Convolutional neural networks' feature maps are analyzed to see if they can be decoupled from cross-channel and spatial correlations.The third base architecture is Xception (short for extreme Inception) since the idea behind it is more advanced than the one on which the Inception design was founded.The Xception design relies heavily on its network's 36 convolutional layers to glean valuable characteristics, as shown in

Evaluation metrics
Precision, recall, and F1 scores were used to objectively evaluate the suggested model's performance in this study.
Accuracy refers to how well authentic and fake photos are identified.To calculate accuracy, one uses: Misclassified manipulated photos are false negatives (FN), whereas correctly classified ones are true positives (TP).The initial photos correctly identified are known as true negatives (TN), while those incorrectly labeled are known as false positives (FP).In the same way that it would be incorrect to label an altered image as genuine, it would also be incorrect to label a genuine image as altered.
An error model is one that consistently generates wrong results.It The f1 score is a combination of the model's recall and its precision.
The minimum is zero, and the highest is one.If it is the highest possible, that means the model is perfect.

RESULTS AND DISCUSSION
A publicly available dataset was used to evaluate the proposed methodology.In this section, we discuss the effectiveness of the proposed models and try to locate an alternative dataset for the same problem for comparative purposes.The enhancements made to the final model were determined after a series of studies.This section details the experiments and analyses that went into this study.Experiments were conducted to assess the effectiveness of the suggested model: 1.The proposed model's efficacy was evaluated on the Lyme rashes disease dataset.
2. Evaluate the suggested model against the best current alternatives.

The proposed deep fusion model performance analysis on the Lyme rashes disease dataset
The proposed model's accuracy and loss graphs on the Lyme rashes disease dataset during training and validation are shown in Figure 6.The On the Lyme rash disease dataset, the suggested technique produces high-quality findings across all evaluation metrics.

Comparison analysis of the deep feature fusion technique with state-of-the-art models
Here, the proposed model is compared to various top-tier methods.
In Table 7, we see the outcomes of a study that compares the most However, data on other skin conditions were included in the analysis.
The proposed model considerably exceeds expectations, with an astonishing 98.97% accuracy, compared to the study reported in Table 7, which only attained 97%.Prior studies have yet to investigate these variables, either alone or in combination, and achieve a higher accuracy than our findings.As a result, we compare our deep fusion model to previous research.A suggestion for Lyme illness was created using the Deep Feature Fusion method, and its accuracy was measured at 98.97%.The results showed that the proposed Deep Feature Fusion technique model performed better than the cited study.[69]

CONCLUSIONS AND FUTURE WORK
Lyme disease is a tick-borne illness prevalent in many parts of the world.According to recent reports, the number of cases is increasing,

Finally, section 5
concludes this paper by summarizing our results, significance, limitations, and open issues for potential future work.
Overfitting might have been avoided by training on a large-scale image dataset, F I G U R E 1 (A) Lyme disease positive (B) Lyme disease negative.which was necessary for the CNN approach to rash detection.It is the initial version of the Lyme rash illness dataset, which includes highresolution versions of every photograph in the dataset.We adjust the dimensions of the dataset to 224 × 224 with the help of python code.
fitting and boost the variety of the dataset.Using a scale transformation, as the parameter value was set to (1/255), the resulting pixel values all fell inside the range of zero to one.The photographs required a specific rotation; therefore, the rotation transformation was applied to them, requiring 25 degrees to achieve the desired outcome.The parameter height shift range was given the value of 0.1 to reposition the training images vertically.The random zoom transformation was applied using a zoom range of 0.2; a value greater than 1.0 indicates that the photographs were magnified, and a number much less than 1.0 indicates that the images were shrunk in size.The snapshot was flipped horizontally with the command Flip.The brightness transformation was employed.Therefore, the zoom range was from 0.5 to 1.0, where 0.0 indicates no brightness and 1.0 indicates maximum possible intelligence.The channel values are randomly transformed during the channel shift by selecting a value randomly from the available range.
and so forth.Convolutional neural networks are the method of choice when dealing with image-based issues.A threshold-based deep learning model was developed to aid in diagnosing Lyme disease using imaging techniques.The data was gathered in-house and pre-processed with an adaptive histogram equalizer to eliminate unwanted variations.The proposed CNN model employs numerous permutations of layering.First, we will train three deep learning pre-trained models to obtain or extract features from the last pooling layer in a vector form to classify affected and non-affected rashes images.These pre-trained models are InceptionV3, DenseNet201, and Xception.Second, the features extracted by the CNN layers will be concatenated and used for retraining the feature fusion's final classification "predictions" layer.

F I G U R E 2
The flowchart of the proposed model deep feature fusion.suggest the AlexNet model and report substantial improvements in object identification and classification in their research.The Top-5 error rate in object recognition and variety is then reduced by developing several convolutional models.Figure 3 of InceptionV3 depicts the top 5 error rates of object identification findings based on Ima-geNet, with GoogleNet displaying the most outstanding recognition results (InceptionV3).The results show that improving recognition performance can be achieved by making the model layer deeper.Regarding object identification, the InceptionV3 model outperforms its predecessor, GoogleNet (InceptionV1).The InceptionV3 model comprises the improved Inception module, the standard convolutional block, and the classifier.The first convolutional Lyme disease component is used in the feature following extraction, as shown in Figure 3. Layer types that switch between convolutional and max-pooling, in which there is a Network-In-Network, 63 form the foundation upon which the improved Inception module was built.This technique performs multiscale convolutions in parallel and then combines the convolutional results from each branch.It has been found that the introduction of auxiliary classifiers improves the consistency of training results, accelerates the rate at which gradients converge, and alleviates over-fitting and vanishing gradient issues.The use of additional classifiers allows for these advantages to be realized.To reduce the required number of feature channels and quicken the training process, InceptionV3 uses the 1 × 1 convolutional kernel.F I G U R E 3 InceptionV3 architecture diagram.Furthermore, the massive convolution is split into smaller convolutions, reducing the total computation cost and the number of parameters.Therefore, this model is often employed for the function of transfer learning.In conclusion, InceptionV3's superior support system.
However, the conventional L-layer Convolutional Neural Network (CNN) has a relationship between its layers that is proportional to L(L+1), meaning that each layer builds upon the information from the previous layer.One such 201-layer convolutional neural network is the DenseNet201.A network pretrained on over a million images is loaded from the ImageNet database.A thousand distinct types of objects, such as animals, plants, and technological gadgets, can be identified in a single image by the internet.As a result of this procedure, the network contains full feature representations for many different kinds of images.The most universally applicable input for the network is a 224-by-224-pixel image.Each layer in the network is equipped with a filter-based convolution, batch normalization (BN), and ReLU activation, as illustrated in

Figure 4 .
Figure 4.It is possible to reduce the overfitting during training by initially passing through a batch normalization stage, a matrix representing each picture pixel.If the value is lower than that, ReLu will be triggered

Figure 5 .F I G U R E 4
Figure 5.After the input on top of a convolutional foundation, add a logistic regression layer.Additionally, fully-connected layers can be included before the coating of logistic regression that will be the subject of the upcoming discussion of experimental evaluation.All modules containing the 36 convolutional layers are surrounded by linear residual connections, except for the first and end modules.The Xception architecture is a linear stack of depth-separable convolution layers connected by residual links.

5 )F I G U R E 5
is how we keep track of the many times we were wrong.Error = ((T P + F N)) ∕ (T P + T N + F N + F P) × 100 (4) The PPV measures how many times the model made correct positive predictions.P recision = (T P) ∕ (T P + F P) × 100 (Xception architecture diagram.TA B L E 5 Experimental setup configuration details. sensitivity, is the fraction of true positives that the model captures.Recall = (T P) ∕ (T P + F N) × 100(6)

F 1
Score = 2 × (P recision × Recall) ∕ (P recision + Recall) Colab Pro account, we trained and tested the proposed and three baseline models.The next step was to train the model to automatically extract the deep feature from the last pooling layer of the underlying models.The proposed model and its three transfer learning basis models go through six iterations of training at a learning rate of 0.0001.The networks are trained using Adam (71), an adaptive firstorder gradient-based optimization method.The batch size will be 16.If there is no improvement in the validation loss after five iterations, the learning rate is halved.Binary categorical cross entropy loss functions are used during training for all studies.When no other loss function is specified, cross-entropy is used.
highlighting the importance of adequate identification and classification methods.Current methods of diagnosis rely on a combination of clinical presentation, laboratory tests, and patient history.However, these methods can be time-consuming and costly, and false-negative results can occur.This research proposes a new technique for detecting Lyme disease based on deep learning algorithms.The proposed technique, called Deep Feature Fusion, utilizes a combination of three deep learning-based models, namely DenseNet201, InceptionV3, and Xception.The models were trained and evaluated using a dataset of Lyme rashes disease images collected from patients with different disease subtypes.The proposed technique achieved an accuracy of 98.97%, significantly higher than other existing techniques.This study provides a promising avenue for the future detection and management of Lyme disease.Further research can be conducted to expand the use of the Deep Feature Fusion technique to identify other tick-borne illnesses, such as Rocky Mountain spotted fever, and exclude Lyme disease when assessing the entire spectrum of skin diseases.The proposed technique can improve the accuracy and efficiency of Lyme disease detection, leading to earlier diagnosis and more effective treatment.This research presents a valuable contribution to Lyme disease detection and classification.The proposed technique can be integrated into clinical practice to improve the accuracy and efficiency of Lyme disease diagnosis, and it has the potential to impact the management of this growing global concern significantly.

TA B L E 7
66sessment of the proposed strategy concerning state-of-the-art paradigms.ResNet50 to detect erythema migrans and other perplexing skin disorders; they achieved 95% accuracy in recognizing erythema migrans, but this only holds if the dataset is legitimate.For early Lyme disease identification based on gene expression, Servellita et al.31created a diagnostic classifier with a 95.2% accuracy.Justin et al.66trained a CNN to identify tick bites using a photo dataset collected via a mobile app.